Have you ever thought about how to use Generative AI for requirements management?
I recently spoke to an engineering manager at an automotive supplier. He talked about how when they receive an RFQ (Request for Quote) from an automotive manufacturer, there are just way too many specifications, regulations, and standards that have to be analyzed and adhered to in the response. It can be overwhelming.
The first step the engineering lead typically takes is to organize the RFQ into tasks and assign those tasks to specific individuals. But who? My contact indicated that he’d assign tasks to:
- Hardware Engineers
- Software Engineers
- Mechanical Engineers
- Electrical Engineers
- Testing and QA Specialists
Each individual would then be responsible for responding to particular tasks in the customer spec. Those individuals would then need to research: Can we do this? Have we responded to similar requirements before? If so, how similar were they? Can we reuse some of the schematics and code, or do we need to start over from scratch?
As you may already know, these customer specs can be hundreds of pages long. So can the responses. Furthermore, when researching how to comply with given standards and regulations, those documents can be thousands of pages long. It’s critical to find gaps and conflicts between requirements from past responses and requirements from the current bid.
An RFQ response can easily take 4-12 weeks long with many, many people working on it. It’s critical to invest the least amount of time possible for the best possible bid, because it is unknown whether the bid will be successful or not. If it’s unsuccessful, all that work will have been for nothing.
What if there was a tool that automated this process? That could go through each customer requirement and find prior, relevant responses from the past? And what if that tool could even identify where gaps or conflicts might occur?
Of course, the time it takes to respond to these OEM requests vary based on factors like the complexity of the requested parts, the supplier’s internal processes, and the nature of the relationship with the OEM.
For complex parts, such as custom components or those requiring significant engineering development, response times can be even longer. These RFQs often require collaboration with design and engineering teams to ensure feasibility and pricing accuracy.
For projects that are part of strategic partnerships, joint ventures, or programs that involve significant collaboration, the response timeline may also extend. Much of this depends on the exact development needs.
This may sound like a distant pipe dream, but it’s not. We at Humaxa are working with automotive industry project managers, technical project leads, and chief engineers everywhere to take away the laborious pain of RFQ response.
We listen with our ears piqued to discover every nuanced part of the RFQ response process and build a future where draft responses – to be reviewed by human beings – can be generated in minutes, not days or months.
Would you like to collaborate on building the future of requirements management with us?
Carolyn Peer
CEO/Co-founder, Humaxa
